Overview

Dataset statistics

Number of variables22
Number of observations4366
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory750.5 KiB
Average record size in memory176.0 B

Variable types

Numeric22

Warnings

purchases is highly correlated with quantity_p and 2 other fieldsHigh correlation
devolutions is highly correlated with quantity_p and 3 other fieldsHigh correlation
recency_p is highly correlated with avg_recency_days and 1 other fieldsHigh correlation
recency_d is highly correlated with invoices_dHigh correlation
quantity_p is highly correlated with purchases and 4 other fieldsHigh correlation
quantity_d is highly correlated with devolutions and 3 other fieldsHigh correlation
invoices_p is highly correlated with purchases and 2 other fieldsHigh correlation
invoices_d is highly correlated with recency_d and 1 other fieldsHigh correlation
avg_ticket is highly correlated with devolutions and 3 other fieldsHigh correlation
avg_recency_days is highly correlated with recency_p and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with devolutions and 3 other fieldsHigh correlation
avg_variety is highly correlated with behavior_scoreHigh correlation
gross_revenue is highly correlated with purchases and 1 other fieldsHigh correlation
relative_revenue is highly correlated with relative_quantity and 2 other fieldsHigh correlation
relative_quantity is highly correlated with relative_revenue and 1 other fieldsHigh correlation
monetary_score is highly correlated with relative_revenue and 2 other fieldsHigh correlation
ticket_score is highly correlated with relative_revenue and 1 other fieldsHigh correlation
recency_score is highly correlated with recency_p and 1 other fieldsHigh correlation
behavior_score is highly correlated with avg_varietyHigh correlation
purchases is highly correlated with quantity_p and 4 other fieldsHigh correlation
devolutions is highly correlated with recency_d and 4 other fieldsHigh correlation
recency_p is highly correlated with invoices_p and 2 other fieldsHigh correlation
recency_d is highly correlated with devolutions and 4 other fieldsHigh correlation
quantity_p is highly correlated with purchases and 4 other fieldsHigh correlation
quantity_d is highly correlated with devolutions and 4 other fieldsHigh correlation
invoices_p is highly correlated with purchases and 7 other fieldsHigh correlation
invoices_d is highly correlated with devolutions and 4 other fieldsHigh correlation
avg_ticket is highly correlated with avg_variety and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with recency_p and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with purchases and 3 other fieldsHigh correlation
avg_variety is highly correlated with avg_ticket and 2 other fieldsHigh correlation
purchases_pday is highly correlated with invoices_pHigh correlation
gross_revenue is highly correlated with purchases and 4 other fieldsHigh correlation
relative_revenue is highly correlated with devolutions and 4 other fieldsHigh correlation
relative_quantity is highly correlated with devolutions and 4 other fieldsHigh correlation
monetary_score is highly correlated with purchases and 4 other fieldsHigh correlation
ticket_score is highly correlated with avg_ticket and 2 other fieldsHigh correlation
recency_score is highly correlated with recency_p and 2 other fieldsHigh correlation
behavior_score is highly correlated with avg_ticket and 2 other fieldsHigh correlation
purchases is highly correlated with quantity_p and 3 other fieldsHigh correlation
devolutions is highly correlated with recency_d and 4 other fieldsHigh correlation
recency_p is highly correlated with avg_recency_days and 1 other fieldsHigh correlation
recency_d is highly correlated with devolutions and 4 other fieldsHigh correlation
quantity_p is highly correlated with purchases and 3 other fieldsHigh correlation
quantity_d is highly correlated with devolutions and 4 other fieldsHigh correlation
invoices_p is highly correlated with purchases and 2 other fieldsHigh correlation
invoices_d is highly correlated with devolutions and 4 other fieldsHigh correlation
avg_ticket is highly correlated with ticket_scoreHigh correlation
avg_recency_days is highly correlated with recency_p and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with quantity_pHigh correlation
avg_variety is highly correlated with behavior_scoreHigh correlation
gross_revenue is highly correlated with purchases and 3 other fieldsHigh correlation
relative_revenue is highly correlated with devolutions and 4 other fieldsHigh correlation
relative_quantity is highly correlated with devolutions and 4 other fieldsHigh correlation
monetary_score is highly correlated with purchases and 3 other fieldsHigh correlation
ticket_score is highly correlated with avg_ticketHigh correlation
recency_score is highly correlated with recency_p and 1 other fieldsHigh correlation
behavior_score is highly correlated with avg_varietyHigh correlation
purchases is highly correlated with devolutions and 8 other fieldsHigh correlation
avg_variety is highly correlated with behavior_scoreHigh correlation
devolutions is highly correlated with purchases and 5 other fieldsHigh correlation
quantity_d is highly correlated with purchases and 5 other fieldsHigh correlation
df_index is highly correlated with avg_recency_days and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with purchases and 5 other fieldsHigh correlation
monetary_score is highly correlated with purchases and 5 other fieldsHigh correlation
avg_recency_days is highly correlated with df_index and 2 other fieldsHigh correlation
relative_revenue is highly correlated with monetary_score and 2 other fieldsHigh correlation
quantity_p is highly correlated with purchases and 6 other fieldsHigh correlation
invoices_d is highly correlated with purchases and 2 other fieldsHigh correlation
invoices_p is highly correlated with purchases and 3 other fieldsHigh correlation
gross_revenue is highly correlated with purchases and 4 other fieldsHigh correlation
behavior_score is highly correlated with avg_variety and 2 other fieldsHigh correlation
recency_p is highly correlated with df_index and 1 other fieldsHigh correlation
recency_score is highly correlated with avg_recency_daysHigh correlation
relative_quantity is highly correlated with monetary_score and 3 other fieldsHigh correlation
avg_ticket is highly correlated with purchases and 5 other fieldsHigh correlation
ticket_score is highly correlated with devolutions and 8 other fieldsHigh correlation
devolutions is highly skewed (γ1 = 47.38221518) Skewed
quantity_p is highly skewed (γ1 = 30.93123636) Skewed
quantity_d is highly skewed (γ1 = 45.51559535) Skewed
avg_ticket is highly skewed (γ1 = 46.64636797) Skewed
avg_basket_size is highly skewed (γ1 = 48.13011902) Skewed
gross_revenue is highly skewed (γ1 = 21.69086914) Skewed
df_index has unique values Unique
customer_id has unique values Unique
devolutions has 2778 (63.6%) zeros Zeros
quantity_d has 2778 (63.6%) zeros Zeros
invoices_d has 2778 (63.6%) zeros Zeros
behavior_score has 158 (3.6%) zeros Zeros

Reproduction

Analysis started2021-06-20 11:55:00.395274
Analysis finished2021-06-20 11:56:01.082193
Duration1 minute and 0.69 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct4366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2837.469079
Minimum0
Maximum5970
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:01.187193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile229.25
Q11303.25
median2733.5
Q34423.75
95-th percentile5635.75
Maximum5970
Range5970
Interquartile range (IQR)3120.5

Descriptive statistics

Standard deviation1758.395649
Coefficient of variation (CV)0.6197056603
Kurtosis-1.238510832
Mean2837.469079
Median Absolute Deviation (MAD)1550
Skewness0.1035389668
Sum12388390
Variance3091955.26
MonotonicityStrictly increasing
2021-06-20T08:56:01.329879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
12541
 
< 0.1%
12661
 
< 0.1%
33111
 
< 0.1%
53561
 
< 0.1%
33071
 
< 0.1%
12581
 
< 0.1%
33031
 
< 0.1%
53481
 
< 0.1%
14261
 
< 0.1%
Other values (4356)4356
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
59701
< 0.1%
59631
< 0.1%
59621
< 0.1%
59601
< 0.1%
59581
< 0.1%
59541
< 0.1%
59531
< 0.1%
59521
< 0.1%
59511
< 0.1%
59501
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct4366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15301.45121
Minimum12346
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:01.468316image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12614.25
Q113814.25
median15303.5
Q316779.75
95-th percentile17984.75
Maximum18287
Range5941
Interquartile range (IQR)2965.5

Descriptive statistics

Standard deviation1722.144646
Coefficient of variation (CV)0.1125477984
Kurtosis-1.195850103
Mean15301.45121
Median Absolute Deviation (MAD)1484.5
Skewness0.0001007032311
Sum66806136
Variance2965782.181
MonotonicityNot monotonic
2021-06-20T08:56:01.596175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163841
 
< 0.1%
136441
 
< 0.1%
136561
 
< 0.1%
177501
 
< 0.1%
157011
 
< 0.1%
136521
 
< 0.1%
177461
 
< 0.1%
177421
 
< 0.1%
177381
 
< 0.1%
158611
 
< 0.1%
Other values (4356)4356
99.8%
ValueCountFrequency (%)
123461
< 0.1%
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182811
< 0.1%
182801
< 0.1%
182781
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%

purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4280
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2040.183367
Minimum0
Maximum280206.02
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:01.731227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile107.4875
Q1303.3075
median665.82
Q31654.0875
95-th percentile5775.52
Maximum280206.02
Range280206.02
Interquartile range (IQR)1350.78

Descriptive statistics

Standard deviation8962.013605
Coefficient of variation (CV)4.392749078
Kurtosis480.9126511
Mean2040.183367
Median Absolute Deviation (MAD)465.995
Skewness19.38102887
Sum8907440.58
Variance80317687.85
MonotonicityNot monotonic
2021-06-20T08:56:01.863962image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033
 
0.8%
76.324
 
0.1%
35.43
 
0.1%
4403
 
0.1%
363.653
 
0.1%
153
 
0.1%
71.42
 
< 0.1%
1202
 
< 0.1%
5902
 
< 0.1%
251.212
 
< 0.1%
Other values (4270)4309
98.7%
ValueCountFrequency (%)
033
0.8%
3.751
 
< 0.1%
6.21
 
< 0.1%
6.91
 
< 0.1%
12.751
 
< 0.1%
13.31
 
< 0.1%
153
 
0.1%
171
 
< 0.1%
20.82
 
< 0.1%
25.52
 
< 0.1%
ValueCountFrequency (%)
280206.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
143825.061
< 0.1%
124914.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
81024.841
< 0.1%
77183.61
< 0.1%

devolutions
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1169
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.019787
Minimum0
Maximum168469.6
Zeros2778
Zeros (%)63.6%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:02.008931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316.575
95-th percentile191.135
Maximum168469.6
Range168469.6
Interquartile range (IQR)16.575

Descriptive statistics

Standard deviation2954.51808
Coefficient of variation (CV)21.10071829
Kurtosis2530.699539
Mean140.019787
Median Absolute Deviation (MAD)0
Skewness47.38221518
Sum611326.39
Variance8729177.088
MonotonicityNot monotonic
2021-06-20T08:56:02.149733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02778
63.6%
12.7521
 
0.5%
4.9518
 
0.4%
9.9515
 
0.3%
1515
 
0.3%
5.912
 
0.3%
25.511
 
0.3%
4.2510
 
0.2%
3.759
 
0.2%
19.99
 
0.2%
Other values (1159)1468
33.6%
ValueCountFrequency (%)
02778
63.6%
0.422
 
< 0.1%
0.651
 
< 0.1%
0.771
 
< 0.1%
0.951
 
< 0.1%
1.255
 
0.1%
1.454
 
0.1%
1.641
 
< 0.1%
1.655
 
0.1%
1.72
 
< 0.1%
ValueCountFrequency (%)
168469.61
< 0.1%
77183.61
< 0.1%
392671
< 0.1%
30032.231
< 0.1%
22998.41
< 0.1%
12158.91
< 0.1%
11252.441
< 0.1%
8593.151
< 0.1%
8495.011
< 0.1%
8043.881
< 0.1%

recency_p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.07558406
Minimum0
Maximum373
Zeros35
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:02.291501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q117
median51
Q3147
95-th percentile318
Maximum373
Range373
Interquartile range (IQR)130

Descriptive statistics

Standard deviation102.4445304
Coefficient of variation (CV)1.088959812
Kurtosis0.3775336914
Mean94.07558406
Median Absolute Deviation (MAD)41
Skewness1.235556949
Sum410734
Variance10494.8818
MonotonicityNot monotonic
2021-06-20T08:56:02.430175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1103
 
2.4%
394
 
2.2%
494
 
2.2%
290
 
2.1%
879
 
1.8%
1077
 
1.8%
1774
 
1.7%
772
 
1.6%
971
 
1.6%
2264
 
1.5%
Other values (294)3548
81.3%
ValueCountFrequency (%)
035
 
0.8%
1103
2.4%
290
2.1%
394
2.2%
494
2.2%
548
1.1%
772
1.6%
879
1.8%
971
1.6%
1077
1.8%
ValueCountFrequency (%)
37317
 
0.4%
37218
0.4%
3716
 
0.1%
3693
 
0.1%
3685
 
0.1%
3675
 
0.1%
36610
 
0.2%
36543
1.0%
3646
 
0.1%
3626
 
0.1%

recency_d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct278
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280.8964727
Minimum0
Maximum373
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:02.580076image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q1185
median365
Q3365
95-th percentile365
Maximum373
Range373
Interquartile range (IQR)180

Descriptive statistics

Standard deviation129.9871958
Coefficient of variation (CV)0.4627583768
Kurtosis-0.4666517161
Mean280.8964727
Median Absolute Deviation (MAD)0
Skewness-1.118539076
Sum1226394
Variance16896.67107
MonotonicityNot monotonic
2021-06-20T08:56:02.717680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3652792
63.9%
844
 
1.0%
6439
 
0.9%
3528
 
0.6%
328
 
0.6%
926
 
0.6%
2126
 
0.6%
2922
 
0.5%
2522
 
0.5%
3120
 
0.5%
Other values (268)1319
30.2%
ValueCountFrequency (%)
05
 
0.1%
120
0.5%
213
 
0.3%
328
0.6%
413
 
0.3%
54
 
0.1%
710
 
0.2%
844
1.0%
926
0.6%
108
 
0.2%
ValueCountFrequency (%)
3731
 
< 0.1%
3728
 
0.2%
3712
 
< 0.1%
3692
 
< 0.1%
3688
 
0.2%
3671
 
< 0.1%
3667
 
0.2%
3652792
63.9%
3641
 
< 0.1%
3622
 
< 0.1%

quantity_p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct777
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311.8268438
Minimum0
Maximum80996
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:02.860093image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q160
median115
Q3234
95-th percentile710
Maximum80996
Range80996
Interquartile range (IQR)174

Descriptive statistics

Standard deviation1965.923119
Coefficient of variation (CV)6.304534579
Kurtosis1148.530998
Mean311.8268438
Median Absolute Deviation (MAD)69
Skewness30.93123636
Sum1361436
Variance3864853.711
MonotonicityNot monotonic
2021-06-20T08:56:02.995382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2842
 
1.0%
6040
 
0.9%
6739
 
0.9%
5137
 
0.8%
7236
 
0.8%
5235
 
0.8%
6634
 
0.8%
033
 
0.8%
7033
 
0.8%
9032
 
0.7%
Other values (767)4005
91.7%
ValueCountFrequency (%)
033
0.8%
111
 
0.3%
25
 
0.1%
314
0.3%
46
 
0.1%
52
 
< 0.1%
614
0.3%
73
 
0.1%
85
 
0.1%
98
 
0.2%
ValueCountFrequency (%)
809961
< 0.1%
742151
< 0.1%
386391
< 0.1%
213521
< 0.1%
173761
< 0.1%
171501
< 0.1%
162881
< 0.1%
158531
< 0.1%
133691
< 0.1%
128721
< 0.1%

quantity_d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct186
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.71071919
Minimum-0
Maximum80995
Zeros2778
Zeros (%)63.6%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:03.148978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile-0
Q1-0
median-0
Q33
95-th percentile42
Maximum80995
Range80995
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1678.948683
Coefficient of variation (CV)30.13690556
Kurtosis2109.732802
Mean55.71071919
Median Absolute Deviation (MAD)0
Skewness45.51559535
Sum243233
Variance2818868.681
MonotonicityNot monotonic
2021-06-20T08:56:03.298931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-02778
63.6%
1349
 
8.0%
3173
 
4.0%
690
 
2.1%
289
 
2.0%
475
 
1.7%
545
 
1.0%
1245
 
1.0%
742
 
1.0%
840
 
0.9%
Other values (176)640
 
14.7%
ValueCountFrequency (%)
-02778
63.6%
1349
 
8.0%
289
 
2.0%
3173
 
4.0%
475
 
1.7%
545
 
1.0%
690
 
2.1%
742
 
1.0%
840
 
0.9%
937
 
0.8%
ValueCountFrequency (%)
809951
< 0.1%
742151
< 0.1%
93611
< 0.1%
90141
< 0.1%
48731
< 0.1%
40271
< 0.1%
23991
< 0.1%
23021
< 0.1%
21601
< 0.1%
16851
< 0.1%

invoices_p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct60
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.241868988
Minimum0
Maximum209
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:03.448743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q35
95-th percentile13
Maximum209
Range209
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.681885185
Coefficient of variation (CV)1.810967101
Kurtosis249.7371905
Mean4.241868988
Median Absolute Deviation (MAD)1
Skewness12.0756929
Sum18520
Variance59.01135999
MonotonicityNot monotonic
2021-06-20T08:56:03.579616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11493
34.2%
2831
19.0%
3508
 
11.6%
4387
 
8.9%
5242
 
5.5%
6172
 
3.9%
7143
 
3.3%
898
 
2.2%
968
 
1.6%
1054
 
1.2%
Other values (50)370
 
8.5%
ValueCountFrequency (%)
033
 
0.8%
11493
34.2%
2831
19.0%
3508
 
11.6%
4387
 
8.9%
5242
 
5.5%
6172
 
3.9%
7143
 
3.3%
898
 
2.2%
968
 
1.6%
ValueCountFrequency (%)
2091
< 0.1%
2011
< 0.1%
1241
< 0.1%
971
< 0.1%
931
< 0.1%
911
< 0.1%
861
< 0.1%
731
< 0.1%
631
< 0.1%
621
< 0.1%

invoices_d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8364635822
Minimum0
Maximum47
Zeros2778
Zeros (%)63.6%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:03.704878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum47
Range47
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.136425755
Coefficient of variation (CV)2.55411688
Kurtosis134.1595049
Mean0.8364635822
Median Absolute Deviation (MAD)0
Skewness8.846668074
Sum3652
Variance4.564315005
MonotonicityNot monotonic
2021-06-20T08:56:03.826646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
02778
63.6%
1886
 
20.3%
2308
 
7.1%
3147
 
3.4%
497
 
2.2%
544
 
1.0%
630
 
0.7%
722
 
0.5%
89
 
0.2%
97
 
0.2%
Other values (17)38
 
0.9%
ValueCountFrequency (%)
02778
63.6%
1886
 
20.3%
2308
 
7.1%
3147
 
3.4%
497
 
2.2%
544
 
1.0%
630
 
0.7%
722
 
0.5%
89
 
0.2%
97
 
0.2%
ValueCountFrequency (%)
471
< 0.1%
451
< 0.1%
351
< 0.1%
311
< 0.1%
271
< 0.1%
231
< 0.1%
211
< 0.1%
192
< 0.1%
181
< 0.1%
172
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct4304
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.89335507
Minimum0
Maximum77183.6
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:03.967936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.356321839
Q111.99768757
median17.67979201
Q324.79484848
95-th percentile93.3
Maximum77183.6
Range77183.6
Interquartile range (IQR)12.79716091

Descriptive statistics

Standard deviation1463.214147
Coefficient of variation (CV)21.55165473
Kurtosis2263.833843
Mean67.89335507
Median Absolute Deviation (MAD)6.449171759
Skewness46.64636797
Sum296422.3883
Variance2140995.64
MonotonicityNot monotonic
2021-06-20T08:56:04.122598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033
 
0.8%
154
 
0.1%
76.324
 
0.1%
1794
 
0.1%
18.73
 
0.1%
25.53
 
0.1%
24.42
 
< 0.1%
20.82
 
< 0.1%
302
 
< 0.1%
3582
 
< 0.1%
Other values (4294)4307
98.6%
ValueCountFrequency (%)
033
0.8%
2.1012857141
 
< 0.1%
2.1505882351
 
< 0.1%
2.2411
 
< 0.1%
2.2643751
 
< 0.1%
2.43251
 
< 0.1%
2.4623711341
 
< 0.1%
2.5048760331
 
< 0.1%
2.508371561
 
< 0.1%
2.547049181
 
< 0.1%
ValueCountFrequency (%)
77183.61
< 0.1%
56157.51
< 0.1%
13305.51
< 0.1%
4453.431
< 0.1%
38611
< 0.1%
30961
< 0.1%
2033.11
< 0.1%
2027.861
< 0.1%
1687.21
< 0.1%
1377.0777781
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1276
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.64705894
Minimum0
Maximum373
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:04.274141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q134.66666667
median63.63333333
Q3122.6666667
95-th percentile298.75
Maximum373
Range373
Interquartile range (IQR)88

Descriptive statistics

Standard deviation86.5562299
Coefficient of variation (CV)0.9145157903
Kurtosis1.722870893
Mean94.64705894
Median Absolute Deviation (MAD)35.75
Skewness1.541130343
Sum413229.0593
Variance7491.980935
MonotonicityNot monotonic
2021-06-20T08:56:04.421861image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5329
 
0.7%
3024
 
0.5%
1823
 
0.5%
10622
 
0.5%
3922
 
0.5%
2421
 
0.5%
2520
 
0.5%
2820
 
0.5%
9220
 
0.5%
1519
 
0.4%
Other values (1266)4146
95.0%
ValueCountFrequency (%)
01
 
< 0.1%
14
0.1%
25
0.1%
2.5547945211
 
< 0.1%
39
0.2%
3.2434782611
 
< 0.1%
3.3008849561
 
< 0.1%
3.3333333331
 
< 0.1%
3.51
 
< 0.1%
3.6666666671
 
< 0.1%
ValueCountFrequency (%)
37315
0.3%
37217
0.4%
3717
0.2%
3693
 
0.1%
3685
 
0.1%
3675
 
0.1%
3668
0.2%
36510
0.2%
3645
 
0.1%
3626
 
0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2136
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean251.6187465
Minimum0
Maximum74215
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:04.564029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.54166667
Q191
median160.5666667
Q3270.25
95-th percentile598.6
Maximum74215
Range74215
Interquartile range (IQR)179.25

Descriptive statistics

Standard deviation1308.868296
Coefficient of variation (CV)5.201791654
Kurtosis2541.880608
Mean251.6187465
Median Absolute Deviation (MAD)81.65
Skewness48.13011902
Sum1098567.447
Variance1713136.215
MonotonicityNot monotonic
2021-06-20T08:56:04.707755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033
 
0.8%
10019
 
0.4%
8218
 
0.4%
8817
 
0.4%
12017
 
0.4%
7217
 
0.4%
13617
 
0.4%
7316
 
0.4%
7816
 
0.4%
10616
 
0.4%
Other values (2126)4180
95.7%
ValueCountFrequency (%)
033
0.8%
16
 
0.1%
1.51
 
< 0.1%
25
 
0.1%
32
 
< 0.1%
3.3333333331
 
< 0.1%
48
 
0.2%
53
 
0.1%
5.3333333331
 
< 0.1%
5.6666666671
 
< 0.1%
ValueCountFrequency (%)
742151
< 0.1%
40498.51
< 0.1%
78241
< 0.1%
6009.3333331
< 0.1%
43001
< 0.1%
42801
< 0.1%
3684.476191
< 0.1%
30281
< 0.1%
29241
< 0.1%
28801
< 0.1%

avg_variety
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1043
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.0160071
Minimum0
Maximum300.6470588
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:04.855871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.229166667
Q19.186363636
median16.87301587
Q328
95-th percentile59
Maximum300.6470588
Range300.6470588
Interquartile range (IQR)18.81363636

Descriptive statistics

Standard deviation20.31141942
Coefficient of variation (CV)0.9225750757
Kurtosis18.74952384
Mean22.0160071
Median Absolute Deviation (MAD)8.873015873
Skewness3.031567868
Sum96121.88702
Variance412.5537589
MonotonicityNot monotonic
2021-06-20T08:56:04.985690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
2.3%
1390
 
2.1%
1484
 
1.9%
1082
 
1.9%
1181
 
1.9%
978
 
1.8%
675
 
1.7%
774
 
1.7%
571
 
1.6%
869
 
1.6%
Other values (1033)3563
81.6%
ValueCountFrequency (%)
033
 
0.8%
199
2.3%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
< 0.1%
1.59
 
0.2%
1.5555555561
 
< 0.1%
1.5714285711
 
< 0.1%
1.6666666674
 
0.1%
1.8333333331
 
< 0.1%
ValueCountFrequency (%)
300.64705881
< 0.1%
2191
< 0.1%
203.51
< 0.1%
1911
< 0.1%
1711
< 0.1%
1641
< 0.1%
1581
< 0.1%
1571
< 0.1%
1531
< 0.1%
1491
< 0.1%

purchases_pday
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1243
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4005636754
Minimum0
Maximum17
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:05.127623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.009592344556
Q10.01989485241
median0.04474578328
Q31
95-th percentile1
Maximum17
Range17
Interquartile range (IQR)0.9801051476

Descriptive statistics

Standard deviation0.5606445071
Coefficient of variation (CV)1.399638913
Kurtosis175.8260771
Mean0.4005636754
Median Absolute Deviation (MAD)0.03328437113
Skewness6.66821218
Sum1748.861007
Variance0.3143222633
MonotonicityNot monotonic
2021-06-20T08:56:05.273604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11502
34.4%
250
 
1.1%
033
 
0.8%
0.062518
 
0.4%
0.0277777777817
 
0.4%
0.0238095238117
 
0.4%
0.0909090909115
 
0.3%
0.0833333333314
 
0.3%
0.0294117647113
 
0.3%
0.0769230769213
 
0.3%
Other values (1233)2674
61.2%
ValueCountFrequency (%)
033
0.8%
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
 
< 0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
 
< 0.1%
0.005665722381
 
< 0.1%
0.0056818181822
 
< 0.1%
ValueCountFrequency (%)
171
 
< 0.1%
42
 
< 0.1%
34
 
0.1%
250
 
1.1%
1.1428571431
 
< 0.1%
11502
34.4%
0.751
 
< 0.1%
0.66666666674
 
0.1%
0.55882352941
 
< 0.1%
0.53887399461
 
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct4310
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1900.16358
Minimum-4287.63
Maximum279489.02
Zeros10
Zeros (%)0.2%
Negative42
Negative (%)1.0%
Memory size34.2 KiB
2021-06-20T08:56:05.413950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-4287.63
5-th percentile101.175
Q1293.1875
median648.55
Q31612.625
95-th percentile5632.72
Maximum279489.02
Range283776.65
Interquartile range (IQR)1319.4375

Descriptive statistics

Standard deviation8224.856057
Coefficient of variation (CV)4.328498948
Kurtosis606.3365532
Mean1900.16358
Median Absolute Deviation (MAD)455.265
Skewness21.69086914
Sum8296114.19
Variance67648257.16
MonotonicityNot monotonic
2021-06-20T08:56:05.540737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010
 
0.2%
76.324
 
0.1%
35.43
 
0.1%
153
 
0.1%
363.653
 
0.1%
4403
 
0.1%
215.082
 
< 0.1%
142.52
 
< 0.1%
306.552
 
< 0.1%
428.892
 
< 0.1%
Other values (4300)4332
99.2%
ValueCountFrequency (%)
-4287.631
< 0.1%
-1592.491
< 0.1%
-1192.21
< 0.1%
-1165.31
< 0.1%
-11261
< 0.1%
-840.761
< 0.1%
-611.861
< 0.1%
-451.421
< 0.1%
-295.091
< 0.1%
-227.441
< 0.1%
ValueCountFrequency (%)
279489.021
< 0.1%
256438.491
< 0.1%
187482.171
< 0.1%
132572.621
< 0.1%
123725.451
< 0.1%
113384.141
< 0.1%
88125.381
< 0.1%
65892.081
< 0.1%
62653.11
< 0.1%
59419.341
< 0.1%

relative_revenue
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1548
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9464585758
Minimum-1
Maximum1
Zeros10
Zeros (%)0.2%
Negative42
Negative (%)1.0%
Memory size34.2 KiB
2021-06-20T08:56:05.677478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.7752317671
Q10.9773601379
median1
Q31
95-th percentile1
Maximum1
Range2
Interquartile range (IQR)0.02263986212

Descriptive statistics

Standard deviation0.2102841969
Coefficient of variation (CV)0.2221800322
Kurtosis58.90281829
Mean0.9464585758
Median Absolute Deviation (MAD)0
Skewness-7.214265949
Sum4132.238142
Variance0.04421944345
MonotonicityNot monotonic
2021-06-20T08:56:05.821786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12778
63.6%
-133
 
0.8%
010
 
0.2%
0.92921083721
 
< 0.1%
0.99152041521
 
< 0.1%
0.95347379961
 
< 0.1%
0.99373629821
 
< 0.1%
0.99327610141
 
< 0.1%
-0.9655473381
 
< 0.1%
-0.25619834711
 
< 0.1%
Other values (1538)1538
35.2%
ValueCountFrequency (%)
-133
0.8%
-0.9655473381
 
< 0.1%
-0.59614066321
 
< 0.1%
-0.40645828551
 
< 0.1%
-0.37129840551
 
< 0.1%
-0.33333333331
 
< 0.1%
-0.25619834711
 
< 0.1%
-0.16085899511
 
< 0.1%
-0.057435203531
 
< 0.1%
-2.159280493 × 10-161
 
< 0.1%
ValueCountFrequency (%)
12778
63.6%
0.99983616721
 
< 0.1%
0.99968637181
 
< 0.1%
0.99944877831
 
< 0.1%
0.99941624421
 
< 0.1%
0.99934617991
 
< 0.1%
0.99923023941
 
< 0.1%
0.9990853581
 
< 0.1%
0.99900015071
 
< 0.1%
0.99864060471
 
< 0.1%

relative_quantity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1127
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9273829918
Minimum-1
Maximum1
Zeros18
Zeros (%)0.4%
Negative47
Negative (%)1.1%
Memory size34.2 KiB
2021-06-20T08:56:05.969010image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.6008860448
Q10.9646793976
median1
Q31
95-th percentile1
Maximum1
Range2
Interquartile range (IQR)0.03532060239

Descriptive statistics

Standard deviation0.2302652918
Coefficient of variation (CV)0.2482957892
Kurtosis40.57015379
Mean0.9273829918
Median Absolute Deviation (MAD)0
Skewness-5.825843262
Sum4048.954142
Variance0.05302210461
MonotonicityNot monotonic
2021-06-20T08:56:06.110203image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12778
63.6%
-133
 
0.8%
018
 
0.4%
0.69
 
0.2%
0.98305084758
 
0.2%
0.97183098597
 
0.2%
0.95121951227
 
0.2%
0.96721311487
 
0.2%
0.89473684217
 
0.2%
0.95744680856
 
0.1%
Other values (1117)1486
34.0%
ValueCountFrequency (%)
-133
0.8%
-0.98952879581
 
< 0.1%
-0.98742138361
 
< 0.1%
-0.5036496351
 
< 0.1%
-0.29787234041
 
< 0.1%
-0.25463743681
 
< 0.1%
-0.18231765081
 
< 0.1%
-0.13636363641
 
< 0.1%
-0.11940298511
 
< 0.1%
-0.11801242241
 
< 0.1%
ValueCountFrequency (%)
12778
63.6%
0.99984463611
 
< 0.1%
0.99938949941
 
< 0.1%
0.99931491211
 
< 0.1%
0.99928901531
 
< 0.1%
0.99903567981
 
< 0.1%
0.99871299871
 
< 0.1%
0.99866666671
 
< 0.1%
0.99861303741
 
< 0.1%
0.99853049231
 
< 0.1%

monetary_score
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4230
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.780838168
Minimum-3
Maximum5.446364751
Zeros0
Zeros (%)0.0%
Negative54
Negative (%)1.2%
Memory size34.2 KiB
2021-06-20T08:56:06.252599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile2.005073211
Q12.467145392
median2.811943454
Q33.207533381
95-th percentile3.750717313
Maximum5.446364751
Range8.446364751
Interquartile range (IQR)0.7403879891

Descriptive statistics

Standard deviation0.8418262849
Coefficient of variation (CV)0.3027239393
Kurtosis25.21260616
Mean2.780838168
Median Absolute Deviation (MAD)0.3700442532
Skewness-3.806544442
Sum12141.13944
Variance0.708671494
MonotonicityNot monotonic
2021-06-20T08:56:06.377160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-354
 
1.2%
1.8826383624
 
0.1%
1.1760912593
 
0.1%
2.3170181013
 
0.1%
1.5490032623
 
0.1%
2.5606835923
 
0.1%
2.6434526763
 
0.1%
2.3176455432
 
< 0.1%
1.8987251822
 
< 0.1%
2.3096301672
 
< 0.1%
Other values (4220)4287
98.2%
ValueCountFrequency (%)
-354
1.2%
0.46239799791
 
< 0.1%
0.57403126771
 
< 0.1%
1.1055101851
 
< 0.1%
1.1760912593
 
0.1%
1.2304489211
 
< 0.1%
1.3180633352
 
< 0.1%
1.406540181
 
< 0.1%
1.4771212551
 
< 0.1%
1.4857214261
 
< 0.1%
ValueCountFrequency (%)
5.4463647511
< 0.1%
5.4089832111
< 0.1%
5.2729599721
< 0.1%
5.1224538391
< 0.1%
5.0924590421
< 0.1%
5.054552311
< 0.1%
4.9451010031
< 0.1%
4.8188332171
< 0.1%
4.7969425641
< 0.1%
4.7739278241
< 0.1%

ticket_score
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4289
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.530895657
Minimum-3
Maximum5.188555027
Zeros0
Zeros (%)0.0%
Negative33
Negative (%)0.8%
Memory size34.2 KiB
2021-06-20T08:56:06.519447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile0.9401499329
Q11.380127542
median1.548507129
Q31.695391211
95-th percentile2.270908637
Maximum5.188555027
Range8.188555027
Interquartile range (IQR)0.3152636686

Descriptive statistics

Standard deviation0.5563528036
Coefficient of variation (CV)0.3634165405
Kurtosis32.75740056
Mean1.530895657
Median Absolute Deviation (MAD)0.1541124923
Skewness-3.523114853
Sum6683.89044
Variance0.309528442
MonotonicityNot monotonic
2021-06-20T08:56:06.655712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-333
 
0.8%
1.4771212555
 
0.1%
1.7075701764
 
0.1%
2.1836683574
 
0.1%
2.5538830274
 
0.1%
1.6190933313
 
0.1%
1.5728716023
 
0.1%
1.6618126863
 
0.1%
2.6180480972
 
< 0.1%
1.5587085712
 
< 0.1%
Other values (4279)4303
98.6%
ValueCountFrequency (%)
-333
0.8%
0.62351510361
 
< 0.1%
0.63358726131
 
< 0.1%
0.65147185221
 
< 0.1%
0.6559783471
 
< 0.1%
0.68708284461
 
< 0.1%
0.6923835071
 
< 0.1%
0.69981623311
 
< 0.1%
0.70042186371
 
< 0.1%
0.70706732641
 
< 0.1%
ValueCountFrequency (%)
5.1885550271
< 0.1%
5.0504377611
< 0.1%
4.4250611951
< 0.1%
3.9497246261
< 0.1%
3.8877297971
< 0.1%
3.7918309481
< 0.1%
3.6091887361
< 0.1%
3.6080679641
< 0.1%
3.5281965621
< 0.1%
3.4399884661
< 0.1%

recency_score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1276
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.383933061
Minimum0.51708769
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:06.799122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.51708769
5-th percentile0.5775914349
Q10.899235974
median1.243860756
Q31.674436717
95-th percentile2.581988897
Maximum10
Range9.48291231
Interquartile range (IQR)0.7752007426

Descriptive statistics

Standard deviation0.7165687406
Coefficient of variation (CV)0.5177770231
Kurtosis12.35621029
Mean1.383933061
Median Absolute Deviation (MAD)0.3813138501
Skewness2.35908523
Sum6042.251746
Variance0.51347076
MonotonicityNot monotonic
2021-06-20T08:56:06.935081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.36082763529
 
0.7%
1.7960530224
 
0.5%
2.29415733923
 
0.5%
1.5811388322
 
0.5%
0.96673648922
 
0.5%
221
 
0.5%
1.85695338220
 
0.5%
1.03695169520
 
0.5%
1.96116135120
 
0.5%
2.519
 
0.4%
Other values (1266)4146
95.0%
ValueCountFrequency (%)
0.5170876915
0.3%
0.517780373117
0.4%
0.51847584747
0.2%
0.51987524493
 
0.1%
0.52057920635
 
0.1%
0.52128603515
 
0.1%
0.5219957518
0.2%
0.522708373510
0.2%
0.52342392265
 
0.1%
0.52486388116
 
0.1%
ValueCountFrequency (%)
101
 
< 0.1%
7.0710678124
0.1%
5.7735026925
0.1%
5.3038685121
 
< 0.1%
59
0.2%
4.8544385641
 
< 0.1%
4.8219320621
 
< 0.1%
4.8038446141
 
< 0.1%
4.7140452081
 
< 0.1%
4.6291004991
 
< 0.1%

behavior_score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2069
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.182077829
Minimum0
Maximum4.68088823
Zeros158
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-20T08:56:07.074746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3699090526
Q11.692693537
median2.292256071
Q32.781870214
95-th percentile3.475445187
Maximum4.68088823
Range4.68088823
Interquartile range (IQR)1.089176677

Descriptive statistics

Standard deviation0.8823341137
Coefficient of variation (CV)0.4043550151
Kurtosis0.1378642302
Mean2.182077829
Median Absolute Deviation (MAD)0.5376906246
Skewness-0.5371999585
Sum9526.951804
Variance0.7785134883
MonotonicityNot monotonic
2021-06-20T08:56:07.214766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0158
 
3.6%
266
 
1.5%
1.6901960864
 
1.5%
1.80617997463
 
1.4%
1.55630250162
 
1.4%
2.22788670561
 
1.4%
1.90848501961
 
1.4%
2.29225607160
 
1.4%
0.602059991359
 
1.4%
2.0827853758
 
1.3%
Other values (2059)3654
83.7%
ValueCountFrequency (%)
0158
3.6%
1.871197496 × 10-111
 
< 0.1%
0.00093637644681
 
< 0.1%
0.0099360696211
 
< 0.1%
0.016589847611
 
< 0.1%
0.016979236061
 
< 0.1%
0.053345212551
 
< 0.1%
0.054985335821
 
< 0.1%
0.058344771
 
< 0.1%
0.078979759041
 
< 0.1%
ValueCountFrequency (%)
4.680888231
< 0.1%
4.5620667341
< 0.1%
4.4659922211
< 0.1%
4.4296876961
< 0.1%
4.3973141741
< 0.1%
4.3917993051
< 0.1%
4.3693828621
< 0.1%
4.3409794181
< 0.1%
4.3247304691
< 0.1%
4.3167249841
< 0.1%

Interactions

2021-06-20T08:55:03.097276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:03.228089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:03.335358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:03.443153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:03.566539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:03.678911image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:03.790410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:03.907020image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:04.027956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:04.132747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:04.236541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:04.366160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:04.488245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:04.614813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:04.740013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:04.863630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:04.975271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:05.089840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:05.218537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:05.329502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:05.433728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:05.537530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:05.654412image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:05.766309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:05.872792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:05.991307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:06.113910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:06.227937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:06.349195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:06.467221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:06.740017image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:06.852680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:06.966431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:07.088542image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:07.199105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:07.323679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:07.441366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:07.552573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:07.657002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:07.782208image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:07.907061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:08.011189image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:08.114433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:08.229621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:08.341312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:08.449530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:08.562766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:08.676829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:08.788287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:08.895677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:09.002559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:09.115424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:09.224740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:09.324303image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:09.423440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:09.547853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:09.670336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:09.785107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:09.890730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:10.002176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:10.117931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:10.238513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:10.355205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:10.459287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:10.564139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:10.665376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:10.767008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:10.889306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:11.002885image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:11.115454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:11.233766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:11.542803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:11.671106image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:11.801434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:11.929350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:12.045277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:12.161209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:12.291373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:12.424525image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:12.556391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:12.683539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:12.815073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:12.941180image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:13.074928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:13.207829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:13.332052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:13.459853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:13.584304image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:13.707443image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:13.830570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:13.955153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:14.075743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:14.198273image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:14.319047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:14.435801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:14.559650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:14.687420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:14.800111image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:14.909604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:15.031487image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:15.152160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:15.275825image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:15.393800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:15.515890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:15.628816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:15.753155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:15.878218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:15.991256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:16.098165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:16.207471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:16.316475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:16.443211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:16.569827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:16.691811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-06-20T08:55:40.642874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:40.759712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:40.868207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:40.974079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:41.090436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:41.203868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:41.318101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:41.432607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:41.553138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:41.663152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:41.779891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:41.897782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:42.002883image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:42.107527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:42.213946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:42.321179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:42.426411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:42.532390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:42.634632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:42.741119image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:42.847145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:42.953462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:43.062639image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:43.172664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:43.271848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:43.783155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:43.906301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:44.013814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:44.121843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:44.225793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:44.332763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:44.435440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:44.544812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:44.654778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:44.752447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:44.849957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:44.950346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:45.049466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:45.164417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:45.278343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:45.391956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:45.511714image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:45.634348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:45.757066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:45.878299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:46.003506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:46.115093image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:46.225693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:46.346047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:46.463643image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:46.582053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:46.698751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:46.816924image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:46.934970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:47.070554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:47.207162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:47.328406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:47.449615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:47.562937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:47.682978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:47.811971image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:47.937220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:48.052419image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:48.183806image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:48.314017image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:48.435869image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:48.562720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:48.697260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:48.818871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:48.930966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:49.063853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:49.194775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:49.327007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:49.450406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:49.581881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:49.707115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:49.830109image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:49.964223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:50.084586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:50.198484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:50.311214image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:50.434944image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:50.542207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:50.641609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:50.737769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:50.848991image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:50.958821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:51.066567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:51.169415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:51.282169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-06-20T08:55:51.588683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:51.695858image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:51.807720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:51.915308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:52.024330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:52.125861image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:52.240468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:52.355038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:52.447700image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:52.539175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:52.642019image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-06-20T08:55:52.949656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:53.054764image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:53.166844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:53.274523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-06-20T08:55:54.013750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-06-20T08:55:54.330243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:54.437931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:54.552757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:54.659725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:54.751054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:54.850340image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:54.952417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:55.053173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:55.155442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:55.262191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:55.372038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-06-20T08:55:58.605294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-06-20T08:55:59.370056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-06-20T08:55:59.689655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:59.809008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:55:59.929173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:56:00.028641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:56:00.124598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-20T08:56:00.232359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-06-20T08:56:07.358986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-20T08:56:07.623901image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-20T08:56:07.867132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-20T08:56:08.105133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-06-20T08:56:00.479785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-20T08:56:00.935791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idpurchasesdevolutionsrecency_precency_dquantity_pquantity_dinvoices_pinvoices_davg_ticketavg_recency_daysavg_basket_sizeavg_varietypurchases_pdaygross_revenuerelative_revenuerelative_quantitymonetary_scoreticket_scorerecency_scorebehavior_score
00178505391.21102.58372.0302.035.021.034.01.018.152222124.33333350.9705888.73529417.0000005288.630.9626560.2500003.7233431.5599600.8932370.453063
11130473237.54158.4431.031.0132.06.010.08.018.82290726.642857139.10000017.2000000.0291553079.100.9066900.9130433.4884241.5757171.9019912.045657
22125837281.3894.042.056.01569.050.015.03.029.47927120.722222337.33333316.4666670.0403237187.340.9744990.9382333.8565681.7705472.1455962.224704
3313748948.250.0095.0365.0169.0-0.05.00.033.86607193.25000087.8000005.6000000.017921948.251.0000001.0000002.9769231.8307951.0300521.496376
4415100876.00240.90333.0330.048.022.03.03.0292.00000062.16666726.6666671.0000000.073171635.100.5686270.3714292.8028422.7664131.2582180.000000
55152914668.3071.7925.0172.0508.027.015.05.045.32330121.941176140.2000006.8666670.0429804596.510.9697090.8990653.6624281.9573522.0878161.459004
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Last rows

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